... problem is press coverage lags the actual event by days or weeks, so everyone reaches out at the same time once it's on TechCrunch. I built Funding Signals to fix that: it watches the SEC's daily EDGAR Form D index directly (the raw filing, not the press release), scores each raise 0-100, and enriches it with the company's domain and a contact email. Free tier, no card needed: fundingsignals.net Would love feedback, especially from anyone doing ...

... would not be shipping a production database as a solo non-backend developer without it. SEC EDGAR - sounds odd but the free public filing database is the core data source for everything I build. Genuinely underrated. Vercel - zero ops, instant deploys. I could not maintain infrastructure and build product simultaneously without it. Resend - transactional email that just works. I wasted two days on alternatives

... Financial figures come directly from the SEC's XBRL structured data feeds. The AI reads the text and flags red flags. It never touches a number. Every claim links back to the verbatim paragraph in the original EDGAR filing. One click to the source. The result is Sharpread. Forensic SEC filing analysis where the AI reads the filings but never generates the numbers. If you invest in individual stocks and actually read 10-Ks, it might be useful. sharpread.ai What ...

... wanted to give retail investors the kind of deep financial analysis that normally sits behind $20-40/mo paywalls. The catch? The clean data doesn't exist for free. You have to pull it from raw SEC EDGAR filings, and XBRL parsing is an absolute nightmare. Every company tags their financials slightly differently. Edge cases everywhere. I lost count of the nights spent chasing why one company's revenue was off by a rounding quirk or a switched tag. Getting ...

... first. I built Sharpread, a forensic SEC filing analysis tool, on a standard in-process rate limiter for managing requests to the SEC EDGAR database. It worked perfectly in development and in early production. Then I hit a wall. Vercel's serverless architecture spins up multiple Lambda instances concurrently. Each instance had its own rate limiter. They had no idea the others existed. Under any real traffic spike, the instances collectively hammered EDGAR

... debug. Every action call needs full params + response logged so you can understand exactly what the AI did and why. Curious how others are thinking about this: How are you deciding which actions to expose to your AI vs. which ones stay human-controlled? Are you prompting heavily to guide action selection, or letting the model figure it out? What's broken for you that current tool-use implementations don't solve? We just shipped AI Actions on Amarsia with ... ... source sentence, it drops the claim entirely. It does not approximate, summarise, or fill in the gaps. Your point on logging is exactly right. Every Sharpread analysis links each claim back to the highlighted paragraph in the original EDGAR

... team moves to an "okay, we figured it out" mode. Suddenly, the comfort zone exists . Now the cost of staying in it is lower than the cost of leaving it. But the market doesn't stop changing. Competitors don't stop moving. User needs don't stay frozen. So the team keeps executing the same playbook that worked 6 months ago. Not because they're dumb. Because leaving that playbook and failing at something new would hurt more than staying ... ... think about most. I built Sharpread as a solo founder and the single rule I gave myself early was - nothing gets demoed that is not live in production. Every red flag in the demo links to a real SEC EDGAR

... they're being "metered" on every interaction, engagement drops. but if they have a generous base allowance + transparent overages, they're way more comfortable. curious what others have found does anyone have data on conversion rates between pure SaaS vs hybrid vs